Probabilistic decision making system and methods of use

ABSTRACT

Embodiments of this invention comprise modeling a subject&#39;s state and the influence of training scenarios, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. The POMDP is well suited to decision-theoretic planning under uncertainty. Utilizing this model and the resulting training policy with real world subjects creates a surprisingly effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy. POMDP provides a more valid representation of trainee state and training effects, thus it is capable of producing more valid recommendations concerning how to structure training to subjects.

CROSS-REFERENCE TO RELATED APPLICATIONS:

This invention claims benefit of U.S. Provisional Patent Application No.61/035,796 filed on Mar. 12, 2008 and entitled PROBABILISTIC DECISIONMAKING PROCESS, the entire contents of which are herein incorporated byreference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract#FA9550-05-C-0101 awarded by U.S. Air Force. The Government has certainrights in the invention.

REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTINGCOMPACT DISC APPENDIX

Not Applicable

BACKGROUND OF THE INVENTION

1. Technical Field

The subject invention generally relates to decision making. Moreparticularly, the subject invention relates to decision making for teamand individual training.

2. Background

Modern training simulation systems present a unique opportunity.Training designers can generate large libraries of experiential trainingtreatments by systematically varying specific parameters that influencethe challenge to trainees with respect to training objectives. Whenthose training treatments are scenarios, instructors can choose fromthis vast library the scenario that is most appropriate to trainees at agiven time. More dynamic versions of this vision include parameterizedtraining, in which instructors specify scenario parameters prior to eachtraining event, and adaptive training, which automatically adjustsparameters during training.

This is an opportunity in that it enables instructors to fit thetraining more tightly to the needs of trainees. It is a significantchallenge, however, because it may be quite difficult for a humaninstructor to reliably predict which of many candidate scenarios willmost rapidly advance trainees towards expertise. Given that a team hassuccessfully executed some training scenario that presents a largenumber of targets and few threats (or some other configuration of theseor other parameters), is it appropriate to select a scenario thatincreases targets while holding threats constant, increases threatswhile holding targets constant, increases both, or decreases both?

Instructors traditionally address this problem by exploitinginstructional principles, such as the use of hierarchical part tasktraining, in which each skill is taught until students achieve somestandard of performance, and then the next is taught. Alternatively,computer-based training adapts training to the performance of studentsbased on a fixed set of rules concerning which training conditions toapply given a student state.

Traditional solutions such as hierarchical part task trainingpotentially take more training time to achieve a given level of studentperformance and/or achieve lower levels of performance given a maximumtraining time. Opportunities to accelerate and/or improve trainingeffects are not exploited by these solutions.

Traditional solutions such as computer-based training fail when eitherthe student state cannot be accurately judged (i.e., is probabilistic)or the effects of training conditions are uncertain, or both. This isfrequently the case in complex domains, team training, and where thenumber of potential training conditions is large (as in simulation-basedtraining).

BRIEF SUMMARY OF THE INVENTION

It is an object of embodiments of the invention to provide a computerbased system for determining training treatments for a subject on atopic, the system comprises a memory to store at least one actioncomprising at least one training treatment, a processor capable ofexecuting machine instructions and the machine instructions includingmeans for executing a POMDP model to create a training policy todetermine the at least one training treatment to train a subject on atopic.

It is a further object of embodiments of the invention to provide asystem for determining training treatments for a subject wherein thesubject is a team and the training treatments are training treatmentsfor team training.

It is another object of embodiments of the invention to provide a systemfor determining training treatments for a subject wherein the means forexecuting a POMDP model further includes the POMDP model having a state,a transition function, a reward function, an observation and anobservation function. In some of these embodiments, the state comprisesa representation of an expertise state of the subject, the transitionfunction comprises a representation of the probability of an expectedchanged expertise state of the subject after training the subject on thetreatment, the reward function comprises a representation of anobjective and a cost of training the subject on the treatment, theobservation comprises a representation of a measure of the subject, andthe observation function comprises a representation of the probabilityof an expected observation of the subject after training the subject onthe treatment.

It is an object of embodiments of the invention to provide a system fordetermining training treatments for a subject where the representationof the state of expertise of the subject comprises a set of numbersrepresenting the expertise state of the subject on the topic and theobservation comprises a set of numbers representing the measures of thesubject.

It is a further object of embodiments of the invention to provide asystem for determining training treatments for a subject where thetransition function comprises a probability of moving from the expertisestate to the expected changed expertise state conditioned on thetraining treatment given to a subject, the reward function comprises atleast one number, where each number represents a benefit of subjectattaining the expertise state given the training treatment, and theobservation function comprises the probability of an observation giventhe subject's expertise state and training treatment given to thesubject.

It is another object of embodiments of the invention to provide a systemfor determining training treatments for a subject where the step ofutilizing a POMDP model further includes creating a training policy bylinking each state to a training treatment at a node and interconnectingeach node to another node by at least one observation. In someembodiments, the step of utilizing a POMDP model further comprisesapplying the training policy by obtaining the state of the subject,selecting the node having that state and determining the linked trainingtreatment at that node as the training treatment to train the subject onthe topic.

It is an object of embodiments of the invention to provide a programstorage device readable by a machine, tangibly embodying a program ofinstructions executable by the machine to perform the method comprisingthe step of generating a decision making policy from a POMDP model,where the POMDP model comprises a state parameter, an observationparameter and a action parameter, and the action parameter comprisestraining treatments. In some embodiments, the state parameter comprisesthe state of expertise of a subject and the at least one observationparameter comprises a measure of the expertise of the subject.

It is another object of embodiments of the invention to provide theprogram storage device wherein the step of generating a decision makingpolicy further comprises defining the state parameter, the actionparameter and the observation parameter, defining a plurality offunctions comprising a transition function, an observation function anda utility function and generating the decision making policy based onsaid parameters and said functions.

It is a further object of embodiments of the invention to provide theprogram storage device that further includes the steps of determining achanged state of the subject after applying an action parameter,comparing the changed state of the subject to a process threshold,selecting the at least one action parameter from the decision makingpolicy, applying the at least one action parameter to the subject,determining a new changed state of the subject, comparing the newchanged state of the subject to the process threshold and repeating thesteps of selecting the at least one action parameter, applying the atleast one action parameter, determining a new changed state andcomparing the new changed state until the process threshold is met.

It is an object of embodiments of the invention to provide a computerbased method for structuring training treatments for a subject on atopic, said method comprising defining an action comprising at least onetraining treatment and utilizing a POMDP model to create a trainingpolicy to determine the training treatment to train the subject on atopic.

It is another object of embodiments of the invention to provide the acomputer based method for structuring training treatments wherein thesubject is a team and the training treatments are training treatmentsfor team training.

It is a further object of embodiments of the invention to provide amethod for structuring training treatments wherein the step of utilizinga POMDP model further comprises the POMDP model having a state, atransition function, a reward function, an observation and anobservation function.

It is yet another object of embodiments of the invention to provide amethod of structuring training treatments wherein the state comprises arepresentation of an expertise state of the subject, the transitionfunction comprises a representation of the probability of an expectedchanged expertise state of the subject after training the subject on thetreatment, the reward function comprises a representation of anobjective and a cost of training the subject on the treatment, theobservation comprises a representation of a measure of the subject, andthe observation function comprises a representation of the probabilityof an expected observation of the subject after training the subject onthe treatment.

It is another object of embodiments of the invention to provide a methodof structuring training treatments wherein the representation of thestate of expertise of the subject comprises a set of numbersrepresenting the expertise state of the subject on the topic, theobservation comprises a set of numbers representing the measures of thesubject, the transition function comprises a probability of moving fromthe expertise state to the expected changed expertise state given thetraining treatment given to a subject, the reward function furthercomprises at least one number where each number represents a benefit ofsubject attaining the expertise state given the training treatment, andthe observation further comprises the probability of an observationgiven the subject's expertise state and training treatment given to thesubject.

It is a further object of embodiments of the invention to provide amethod of structuring training treatments wherein the step of utilizinga POMDP model further comprises creating a training policy by linkingeach state to at least one training treatment at a node andinterconnecting each node to another node by an observation and the stepof utilizing a POMDP model can further comprises applying the trainingpolicy by obtaining the state of the subject, selecting the node havingthat state and determining the linked training treatment at that node asthe training treatment to train the subject.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

In order that the manner in which the above-recited and other advantagesand features of the invention are obtained, a more particulardescription of the invention briefly described above will be rendered byreference to specific embodiments thereof which are illustrated in theappended drawings. Understanding that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 is a graphic showing one embodiment of a conceptual POMDP model.

FIG. 2 is a graphic showing one embodiment of the decision making systemillustrating the plurality of variables.

FIG. 3 is a graphic showing one embodiment of the decision making systemillustrating the plurality of variables and the interconnection ofselected variables by selected functions.

FIGS. 4A-4B are graphics showing one embodiment of the decision makingsystem illustrating the interconnection of selected variables byselected functions.

FIG. 5 is a graphic representation of the interrelationships of oneembodiment of a policy.

FIG. 6 is a process diagram showing one embodiment of the methods ofinvention.

FIG. 7 is a functional diagram of the machine instructions of onecomputer based embodiment of the invention.

FIG. 8 is a graphic representation of the results of one embodiment ofthe invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is illustrated by, but by no means limited to, thefollowing description of various embodiments.

Embodiments of this invention comprise modeling a subject's state andthe influence of training scenarios, or actions, on that state to createa training policy. Both state and effects of actions are modeled asprobabilistic using Partially Observable Markov Decision Process (POMDP)techniques. The POMDP is well suited to decision-theoretic planningunder uncertainty. Utilizing this model and the resulting trainingpolicy with real world subjects creates a surprisingly effectivedecision aid for instructors to improve learning relative to atraditional scenario selection strategy. POMDP provides a representationof trainee state and training effects by explicitly recognizing theiruncertainty, thus it is capable of producing more valid recommendationsconcerning how to structure training to subjects. Testing ResultsAchieved from one embodiment is described below.

In embodiments, the POMDP captures the dynamic nature of team andindividual skills via the Markov decision process graph. Within thegraph, a single finite discrete variable indexes the current teamexpertise state, and external actions control expertise changes. Thestate changes approximate the dynamics of the team expertise when themodel applies a specific control action to a team. In our context, acontrol action corresponds to selecting a training treatment to trainspecific skills A training treatment may be a mission scenario, atraining objective, or training technique (e.g., presenting problems,explaining principles). Expertise changes are described by a table oftransition probabilities that statistically represent the uncertaineffect on expertise of selecting a specific training treatment for ateam.

The POMDP addresses the problem of partial observability of the truestate of team expertise. While observations about team and individualperformance influence our belief about achieved team skills, the actualor “true” state of skills is not observable. Thus, we can only estimatethe expertise state, interpreting it as “partially observable”.

The POMDP solution represents trainee state and the effects of trainingtreatments as probabilistic. Traditional solutions treat these as known,though in many circumstances (described above) they cannot be known withcertainty. POMDP provides a more valid (i.e., probabilistic)representation of trainee state and training effects, thus it is capableof producing more valid recommendations concerning how to adapt trainingto trainees.

The POMDP model also allows us to treat training treatment selection asboth the control mechanism to change the skills and the testingmechanisms to obtain more knowledge of the true skills state.

Developing a Decision making System with the POMDP Model:

FIG. 1 illustrates the concept of one embodiment of a POMDP decisionmaking system 100. This embodiment of the POMDP decision making systemutilizes a POMDP model having the following variables:

-   -   a finite set of states, S;    -   a finite set of control actions, A;    -   a finite set of observations, Z;    -   a state transition function, τ: S×A→π(S), where π(·) is the        probability distribution over some finite set;    -   an observation function, o: S×A→π(Z); and    -   an immediate reward function, r: S×A→R.

With these variables, as shown in FIG. 1, various actions 140 areselected and applied to subjects to try to change their state 120. Afterthis action 140 is applied, observations 180 are made of the subject totry to determine their changed state 160. Knowing this changed state160, or approximating this state with a belief state, subsequent actionscan be selected based on a decision making policy. This policydetermines the action to be applied to the subject. This policy can alsodetermine the observations that may reflect the state of the subject andit can also predict the effect of the actions on the subject.

Because the variables of this model can be quantified, the processdescribed above can also be pre-populated with variables and functionsthat are expected to reflect the variables, the subjects and theobjectives to come out of the process. The results of pre-populating aPOMDP model is generally described herein as a “policy”. As will bedescribed below, this policy can be used as a decision making tool.

The System Parameters:

The state is the way the subject currently exists and an action willhave the effect of changing the state of the subject. The set of statesof the subject would represent every possible way the subject couldexist. Each of these states would be a state in a MDP or POMDP. In oneembodiment of the invention, the set of states S represents all possiblestates of the expertise of the subject. The subject can be anindividual, a team, a team of teams or expertise. Embodiments of stateinclude, but are not limited to individual skills, team skills and gameposition. A state is defined such that the subject can be in only onestate at a given time.

Control actions, or actions, represent the set of possible alternativechoices you can choose to make. In one embodiment of the invention, theactions set A represents all of the available training/testingtreatments. Other embodiments of actions include, but are not limited totraining scenarios, training objectives, training techniques, gamemoves, organizational decisions, rewards and punishments.

In one embodiment, the observations set Z consists of all possibleobservations about a subject, that is, all possible values of normalizedperformance and process measures such as but not limited to testresults, observations of tasks or other measures intended to approximatethe subjects' state. Other embodiments of observations include but arenot limited to skills, game position and location.

The System Functions:

The functions are used to define how the above parameters changethroughout the process.

The state transition function τ models the uncertainty in the evolutionof expertise states (learning). The transitions specify how each of theactions might change the state of the subject. In embodiments, thetransition function is a representation of the probability of anexpected changed expertise state of the subject after training thesubject.

The observation function o relates the observed measures to the trueunderlying expertise state and treatment selection actions and specifieswhat possible observations may be obtained and how they are influencedby the true expertise state and action in the model. In embodiments, theobservation function comprises a representation of the probability ofobtaining the observation for each state and action in the model.

The immediate utility of performing an action in each of the true statesof the environment is given by the immediate reward function r—which canincorporate a cost of training and a benefit of attaining expertise.

The utility model quantifies the objective of the training and isdescribed using expected cost-reward function

${E\lbrack {\sum\limits_{t = 1}^{K}\; {\gamma^{t}{r\lbrack t\rbrack}}} \rbrack} = {E\lbrack {\sum\limits_{t = 1}^{K}{\gamma^{t}{r( {{s\lbrack t\rbrack},{a\lbrack t\rbrack}} )}}} \rbrack}$

where K is the number of time steps of actions allowed (includinginfinite horizon learning with K=∞), r[t] is a reward obtained at timestep t, and γε (0,1] is a discounting rate controlling how much futurerewards count compared to current rewards (i.e., the smaller this rate,the more initial training gains valued compared to ones obtained later).

Assuming that s_(t),a_(t) are correspondingly expertise state andapplied instructional action (treatment) at time t, the single time-stepreward is calculated as r[t]=r(s[t],a[t]), where r(s_(i),a_(k)) is equalto the reward of transitioning to expertise state s_(i) usinginstructional action (treatment) a_(k).

The System Parameters and Functions in POMDP Model:

A general, an overview of one embodiment of a POMDP model is shown inFIG. 2. As illustrated, the model reflects the iterative process ofstarting with one of a set of states 220, determining one of the set ofactions 240 to be applied to change the state of the subject to one of aset of changed states 260 and then selecting one of a set ofobservations 280 of the subject to try to identify the subject's changedstate 260. Once this is done, the process can start all over again.

Representations of team expertise can be represented in a state-actionmodel, see FIG. 2, equivalent to Markov Decision Process (MDP) graph,where the instructional actions change the team expertise with someuncertainty. The state-action model is uniquely described with a set ofteam expertise states S 220, a set of selectable training treatments, oractions A 240, and state transition function τ 250. That is, ifS={s₁,s₂, . . . ,s_(N)} and A={a₁,a₂, . . . ,a_(M)}, then transitionfunction τ: S×A→π(S) defines the probabilityτ(s_(i),a_(k),s_(j))=Pr{s_(j)|s_(i),a_(k)} that team expertise willchange to a changed state s_(j) if treatment a_(k), such as scenarioinstruction, is applied when team expertise is in state s_(i). Note thatthe model represents the uncertain effect of instructional actions, suchthat

${\sum\limits_{j = 1}^{N}\; {\Pr \{ {{s_{j}s_{i}},a_{k}} \}}} = 1.$

Referring to FIG. 3 the state-action model shows an example of how thetransition and observation functions are used in the model. As shown inFIG. 3, the application of actions 320 can affect the subject'sexpertise to create a changed state 360. The transition functions 350represent the probability of achieving a specific changed state 360. Forexample, if the subject is a state and action 342 is applied, there aremultiple probabilities that changed states may occur. Represented ingeneral by the transition function 350, the different probabilities arerepresented as 30%, 10%, 40% and 20%, elements 352, 354, 356 and 358respectively. Each of these probabilities are associate with theexpected state 360 that will be achieved. For example, there is a 30%probability that state 362 will be achieved and a 10% probability thatchanged state 364 will be achieved. Similarly, the observation function360 relates the probability of an observation being tied to a state, orchanged state 360 of a subject. An example of this relationship isgraphically shown in FIG. 3 where the probabilities of 60%, 15% and 25%,elements 372, 374 and 376 respectively, represent the probability ofobservations 382, 384 and 386 respectively, to reflect the changed stateof the subject as changed state 366.

Another example of these relationship are shown in FIGS. 4A-4B.Referring to FIG. 4A the state-action model shows an example of how thecontrolled instructions of the trainer can affect the dynamics of theteam expertise. For example, if the team does not have any skills inpairing assets (such as weapons) to tasks (such as enemy targets), thentraining a subject on a training treatment containing air and groundtask classes with high appearance frequency would have 30% probabilityof achieving a changed state of having no effects, 10% probability ofachieving a changed state of high level of skills, 40% probability ofacquiring a changed state of some skills for which training is required,and 20% probability that a changed state of adequate skills is achieved.The assigned probabilities reflect the transition function of thataction to create that changed state. Although it is not shown, it isunderstood that there are n number of changed states and the set ofactions associated with changed states and states are not identical.Referring to FIG. 4B, the observation model shows an example of howobservations from the average task accuracy measure are related to theselection of treatments (represented as task classes and taskfrequencies) and the true state of expertise resulting from executing anew treatment. For example, there is a 60% probability that average taskaccuracy observation will range from 60% to 70%, given that the trainingtreatment contained air and ground task classes with high appearancefrequency and that the team achieves some asset-task pairing skills thatrequire training. Although it is not shown, it is understood that thereare n number of observations and the set of observations associated withchanged states, states and actions are not identical.

Generating a Decision making Policy:

The POMDP model represents a set of interrelationships and is used toderive a decision making policy to include structuring trainingtreatments. As used throughout this description a decision making policyis an interrelationship of states, actions and observations that can beused to structure decision making. In one embodiment, an example ofwhich is shown in FIG. 5, POMDP solution is represented as adeterministic transition graph, in which the nodes, 501, 502, 503, 504and 505, correspond to the beliefs about the true state of the expertiseand are associated with the training action. For example, node 502corresponds to action 544 and node 505 corresponds to action 546. Thetransitions between the nodes occur between application of the trainingtreatments and are based on the received observations from previoustraining. For example, after action 544, observations 582, 584 and 586have transitions 592, 594 and 594 respectively. Each directed edge inthe graph in FIG. 5 corresponds to a feasible observation that could bereceived. When the training controller transitions the policy graph to anew state, such as node 505 from transition 594, the correspondingtraining action, such as 546, is selected to be given to trainees forthe next training experience. The policy graph is designed by POMDPsolution algorithms to achieve the greatest amount of expected utility(expected reward of training) over some number of decision steps(training events).

If the states of expertise were observable (such as in Markov DecisionProblems), this policy could be specified as a training action to beperformed at the currently attained state of expertise s. The policy canbe described as a stationary (time-independent) training π(s) ε A ornon-stationary (time-dependent) training π_(t)(s) ε A. In case ofstationary training, which is used when the number of training events isassumed unlimited, π(s) is the training treatment to be applied at thecurrently attained state of expertise s, and it results in the expectedreward to be obtained using this policy (“value function”) which can bewritten as:

$\begin{matrix}{{V_{\pi}(s)} = {E\lbrack {{\sum\limits_{t = 1}^{\infty}\; {\gamma^{t}{r( {{s\lbrack t\rbrack},{a\lbrack t\rbrack}} )}}}\pi} \rbrack}} \\{= {E\lbrack {\sum\limits_{t = 1}^{\infty}\; {\gamma^{t}{r( {{s\lbrack t\rbrack},{\pi ( {s\lbrack t\rbrack} )}} )}}} \rbrack}} \\{= {{r( {s,{\pi (s)}} )} + {\gamma {\sum\limits_{s^{\prime}}^{\;}\; {{V_{\pi}( s^{\prime} )}{\tau ( {s,{\pi (s)},s^{\prime}} )}}}}}}\end{matrix}$

The value function V_(π)(s) for policy π is the unique simultaneoussolution to the above set of linear equations.

The non-stationary training, used when the number of training events isconstrained, is defined as an action/treatment π_(t)(s) to be applied atstate s at time (training event step) t, and it results in the expectedreward to be obtained using this policy (“value function”) which can bewritten as:

$\begin{matrix}{{V_{t,\pi}(s)} = {E\lbrack {\sum\limits_{t = 1}^{K}\; {\gamma^{t}{r( {{s\lbrack t\rbrack},{\pi ( {s\lbrack t\rbrack} )}} )}}} \rbrack}} \\{= {{r( {s,{\pi_{t}(s)}} )} + {\gamma {\sum\limits_{s^{\prime}}\; {{V_{{t + 1},\pi}( s^{\prime} )}{\tau ( {s,{\pi_{t}(s)},s^{\prime}} )}}}}}}\end{matrix}$

As envisioned in a training situation, the true states that teamexpertise takes over time (that is, states of MDP) are not known to thetrainer or the instructional model. They obtain only partialobservations about current state of expertise in the form ofobservations from performance and/or process measures. Theobservation-state relationships are captured using the observation partof the model, as shown in FIG. 4, described by the state set, actionset, and observation function o. That is, if the set of measure outcomesis Z={z₁,z₂, . . . ,z_(L)}, then an observation function defines theprobability Pr{z_(j)|s_(i),a_(k)} that a normalized performance/processmeasure outcome z_(j) is obtained when instruction action a_(k) (atreatment) is applied and team expertise transitions to state s_(i).Sometimes, this probability reflects the dependence of measures on onlythe true expertise state, that is, the probability Pr{z_(j)|s_(i)}.

As shown in FIG. 6, this decision making policy is generated by thesteps of defining the parameters 620, defining the functions 630 andrunning the POMDP model 640. These steps are described in more detailbelow.

Use of the Decision making Policy:

With the decision making policy, the system can include testing,measuring or other observations systems to provide the information thatwill allow a state to be determined which will in turn define theactions necessary. An example of this would include the use of thepolicy in the decision making system described below.

Operational Use of One Embodiment of the Decision making System

For illustration purposes and not for limitation, the followingdescription outlines an operational use of the POMDP decision makingsystem for the situation of training teams using a set of trainingscenarios as the training treatment and human teams as the subjects ofthe training. It is understood that the methods and systems disclosedhave wide applications for any decision making systems or situationswhere a policy can be defined ahead of time. Such additionalapplications for use of the disclosed systems and methods include butare not limited to: deciding directions for equipment such as robots orcars where the actions are movements and observations arecharacteristics of the location; deciding multi-player negotiationoptions where the actions are player negotiation positions andobservations are reactions of the players; and deciding teamcharacteristics of virtual game where the actions are game options andobservations are the reactions of the game and/or players.

Population/Definition of the Parameters:

Parameters for the POMDP model, i.e. feasible observations, actions, andexpertise states are defined by experts and bound by training objectivesor other constraints on the process and system.

States of expertise are defined by experts based on training objectivesand the bounds on feasible complexity of the solution. That is,different decomposition of continuous space of team expertise into a setof discrete expertise states is possible, and the decomposition to beused in the training solution can be customized to the training domain.For example, and not for limitation, the set of states for a POMDP modelrelated to training would be populated with a finite set of variablethat define directly or reference a state of the subject. Examples ofthis as applied to training situation include, but are not limited tostatements of the subject's expertise, percentage attainment of certainskills and other reflections of the subject's state of expertise in atopic. A topic can be a subject area, a set of subject areas or a set ofrequirements. The end result is a set of states.

Actions are defined by experts based on a range of actions that areavailable to influence the state of expertise. For example, and not forlimitation, the set of actions for a POMDP model related to trainingwould be populated with the finite set of actions representing the setof training scenarios possible to be given to the subjects. The endresult is a set of pre-defined actions that can be applied to thesubject.

Observations are pre-defined and are related to measures that can becollected during the experiments. For example, and not for limitation,the set of observations for a POMDP model related to training would bepopulated with a finite set of measures such as test results from thesubject. The observations may or may not relate to state of the subject.The end result is a set of observations.

Population/Definition of the Functions:

Functions for POMDP model, i.e. observation and state transitionprobabilities and rewards, can be defined by experts based on theirknowledge of the environment and team training trends.

The observation function and state transition function can be obtainedby experts based on their knowledge of the effect of training onsubject's states and on the expert's knowledge of how observations may,or may not, relate to the state of the subject. These experts can usetheir knowledge to allocate statistical values and probabilities asneeded by these functions. These functions can also be obtained bystatistical models using the averages of state transitions know from thepreviously conducted experiments. This requires the experiments andteams to be labeled with the true state, which often is not available.In the latter case, the statistical learning algorithms (such asexpectation maximization) can be used to derive the observation functionand state transition function. For example and not for limitation, theobservation function for a POMDP model related to training would bepopulated with percentage values of how related a specific observationmay related the actual state of a subject. The end result is a set ofprobabilities relating observations to states and changed states. Forexample and not for limitation, the state transition function for aPOMDP model related to training would be populated with probabilities ofstates changing given a specific training scenario. The end result is aset of probabilities relating actions to expected changed states.

The reward function is based on the objective of the training to gain acertain level of expertise in a team and cost of training. For exampleand not for limitation, the reward function for a POMDP model related totraining would be populated with numerical representation of anobjective. In one embodiment, the reward function is populated with−1's, 0's and 1's, where a reward for the desired expertise states areequal to 1 and rewards for undesired states are equal to −1, whilerewards for other states are equal to 0. The end result is a set ofrewards with a value for each state and training action.

Generating the Decision making Policy:

The decision making policy can be created by iterating through the setsof states, actions and observations using the defined functions. Thisiteration can be performed through the use of several mathematicalmodels and algorithms as described below. As the variables increase,such as representing states of multiple variables as a vector, the modelshould be carefully built and configured.

At the start of applying the POMDP model, as a result of partialobservability, the decision making system at time t+1 does not know thecurrent state s[t+1] of the team's expertise/knowledge. Instead, thesystem knows the initial belief of the expertise state (priorinformation about the team from its assessment) the history ofobservations z^(t+1)={z[1],z[2], . . . ,z[t+1]} and the system's ownactions a^(t)={a[1],a[2], . . . ,a[t]}. The system can act optimally onthis information by conditioning the training policy on its currentbelief about the state of the team expertise/knowledge at every timestep. The belief state at time t is represented as a vector ofprobabilities b[t]=(b₁[t],b₂[t], . . . ,b_(N)[t]), where b_(i)[t] isequal to the probability that state of the team's knowledge is s_(i) attime

${t( {{\sum\limits_{i = 1}^{N}\; {b_{i}\lbrack t\rbrack}} = 1} )}.$

Then, the belief is updated as

b[t+1]=β(b[t],a[t],z[t+1])

where individually the probabilities are updated:

${b_{i}\lbrack {t + 1} \rbrack} = {{\eta \cdot {o( {s_{i},{a\lbrack t\rbrack},{z\lbrack {t + 1} \rbrack}} )}}{\sum\limits_{j}\; {{b_{j}\lbrack t\rbrack}{\tau ( {s_{j},{a\lbrack t\rbrack},s_{i}} )}}}}$

Here, η is normalization constant.

Then the POMDP-based scenario training policy is defined on the beliefstate, so that we specify the training scenario π(b) ε A to be performedat belief state b, which is updated over time asb[t+1]=β(b[t],a[t],z[t+1]). As the result, the expected reward forpolicy π starting from belief state b is defined to be

$\begin{matrix}{{V_{\pi}(b)} = {E\lbrack {{{\sum\limits_{t = 1}^{\infty}\; {\gamma^{t}{r( {{s\lbrack t\rbrack},{a\lbrack t\rbrack}} )}}}b},\pi} \rbrack}} \\{= {{\sum\limits_{j}\; {b_{j}{r( {s_{j},{\pi (b)}} )}}} + {\gamma {\sum\limits_{z}{{V_{\pi}( {\beta ( {b,{\pi ( {b\lbrack t\rbrack} )},z} )} )}{\Pr ( {{z{\pi ( {b\lbrack t\rbrack} )}},b} )}}}}}}\end{matrix}$

Here, belief-observation component Pr(z|a,b) is found as

${\Pr ( {{za},b} )} = {\sum\limits_{i,j}\; {b_{j} \cdot {o( {s_{j},a,z} )} \cdot {\tau ( {s_{j},a,s_{i}} )}}}$

For a stationary policy, we will have:

${V_{t,\pi}(b)} = {{\sum\limits_{j}\; {{b_{j}\lbrack t\rbrack} \cdot {r( {{s_{j}\lbrack t\rbrack},{\pi ( {b\lbrack t\rbrack} )}} )}}} + {\gamma {\sum\limits_{z}{{V_{{t + 1},\pi}( {\beta ( {{b\lbrack t\rbrack},{\pi ( {b\lbrack t\rbrack} )},z} )} )}{\sum\limits_{i,j}{{b_{j}\lbrack t\rbrack} \cdot {o( {s_{j},{\pi ( {b\lbrack t\rbrack} )},z} )} \cdot {\tau( {s_{j},{\pi( {b\lbrack t } }} }}}}}}}$

Due to the large size of the belief state space, the optimal policy tomaximize the value function V_(π)(b₀), where b₀ is initial belief aboutthe state of team's knowledge or expertise, cannot be derived usingconventional means. Currently, problems of a few hundred states are atthe limits of tractability (Smith and Simmons, 2004). This is due to thefact that most exact algorithms for general POMDPs use a form of dynamicprogramming, which has a computational explosion in the belief statespace (Cassandra, Littman, and Zhang, 1997). Still, these algorithmsprovide a useful finding that a value function can be given by apiece-wise linear and convex representation and transformed into a newsuch function iteratively over time.

Several algorithms for dynamic-programming (DP) updates have beendeveloped, such as one pass (Sondik, 1971), exhaustive enumeration(Monahan, 1982), linear support (Cheng, 1988), and witness (Littman,Cassandra, and Kaelbling, 1996). Out of these algorithms, the witnessalgorithm has been shown to have superior performance (Littman,Cassandra, and Kaelbling, 1996). Combining the benefits of Monahan'senumeration and witness algorithms, an optimal algorithm calledincremental pruning has been developed in (Zhang and Liu, 1996) andenhanced in (Cassandra, Littman, and Zhang, 1997).

The fundamental idea of the DP update is to define the new valuefunction V′ in terms of the given (current) value function V. By viewinga value function as a mapping from the belief state about team knowledgeto the expected reward of training, the solution is found by improvingthis mapping over time. This approach is called value iteration becausethe new single-step update produces value function V′ that is closer tothe optimum value function than previous V. The update is performed asfollows:

$\begin{matrix}{{V^{\prime}(b)} = {\max\limits_{a \in A}( {{\sum\limits_{j}{b_{j}{r( {s_{j},a} )}}} + {\gamma {\sum\limits_{z}{{V( {\beta ( {b,a,z} )} )}{\Pr ( {{za},b} )}}}}} )}} \\{= {\max\limits_{a \in A}{\sum\limits_{z}( {\frac{\sum\limits_{j}{b_{j}{r( {s_{j},a} )}}}{Z} + {\gamma \; {V( {\beta ( {b,a,z} )} )}{\Pr ( {{za},b} )}}} )}}} \\{= {\max\limits_{a \in A}{\sum\limits_{z}{V_{z}^{a}(b)}}}}\end{matrix}$${{where}\mspace{14mu} {V_{z}^{a}(b)}} = {\frac{\sum\limits_{j}{b_{j}{r( {s_{j},a} )}}}{Z} + {\gamma \; {V( {\beta ( {b,a,z} )} )}{\Pr ( {{za},b} )}}}$

The above transformation is relatively simple (Cassandra, Littman, andZhang, 1997) and preserves the piecewise linearity and convexity of thevalue function. This means that if the function V can be expressed as amaximum over a finite set Λ of vectors

${V(b)} = {\max\limits_{\alpha \in \Lambda}\; {b \cdot \alpha}}$

then we can express

${V_{z}^{a}(b)} = {\max\limits_{\alpha \in \Lambda_{z}^{a}}\; {b \cdot \alpha}}$

and the new value function as

${V^{\prime}(b)} = {\max\limits_{\alpha \in \Lambda^{\prime}}\; {b \cdot \alpha}}$

for some finite set of vectors Λ′,Λ^(a) _(z). The sets Λ,Λ′,Λ^(a) _(z)have unique representation of minimum size (Littman, Cassandra, andKaelbling, 1996). FIG. 7 highlights an example of one iteration of thevalue function update for a 2×2 size problem (number of expertisestates=number of actions=number of observations=2). FIG. 7 shows how thespace (between 0 and 1 in this example) of beliefs about the true teamexpertise state (=0 or 1 in this example) is separated into the regionswhere the same action (training) needs to be applied, and that thisseparation is updated over time. Note that complexity of the beliefstate space split does often decreases over time for some iterations(Kaelbling, Littman, and Cassandra, 1998).

The algorithms mentioned above differ in the approach for constructingthe vector sets Λ,Λ′,Λ^(a) _(z). For example, Monahan's exhaustiveenumeration considers every action and belief vector for eachobservation, and is therefore computationally prohibitive. One Passalgorithm of Sondik (1971) starts with an arbitrary belief point,constructs the vector for that point and then defines a set ofconstraints over the belief space where this vector is guaranteed to bedominant. In this algorithm, defined regions are extremely conservative,and might generate same vector for many belief points. Linear supportalgorithm (Cheng, 1988) uses a similar idea to One Pass algorithm butuses fewer constraints. This algorithm picks a belief point, generatesthe vector for that point and then checks the region of that vectors tosee if it is the correct one at all corners (vertices) of the region. Ifnot, it adds the vector at that point and checks its region. If thevalue function is incorrect, the biggest difference will occur at acorner; therefore, if we generate all possible region corners, we areassured of not missing any solutions.

The Witness algorithm (Littman, Cassandra, Kaelbling, 1996) also usesthe same idea as in One Pass algorithm; however, it does not considerall actions at all times. In addition, the Witness algorithm considersonly one observation at a time and concentrates on finding the bestvalue function for each action separately. Once it finds these it willcombine them into the final V′ value function. Finding a belief pointwhere the current observation's choice could be changed, just gives us awitness to the fact that there is a point where we can do better. We canthen take this point and generate the real best vector for it (takinginto account all the observation choices).

The Incremental Pruning algorithm (Zhang, Liu, 1996) combines elementsof Monahan's enumeration and the witness algorithms. This algorithmconstructs sets of vectors for each action individually and then focuseson every observation one at a time. The algorithm finds all differentcombinations of future strategies, while not using the regioncalculation.

To overcome the solution complexity of optimal algorithms, efficientapproximate solutions to POMDP have been proposed (Littman, Cassandra,and Kaelbling, 1995). These algorithms are based on the use of beliefstate-action function

${Q_{a}(b)} = {\max\limits_{\alpha \in \Lambda^{a}}\; {b \cdot \alpha}}$

for which

${V(b)} = {\max\limits_{a}\; {Q_{a}(b)}}$

The algorithms utilize the update of the sets of vectors α usingreplicated Q-learning or linear Q-learning (Littman, Cassandra, andKaelbling, 1995). The linear Q-learning update can be seen as the updateof the vectors during the search in belief state:

${{\Delta\alpha}_{a}(s)} = {\mu \; {b(s)}( {r + {\gamma \mspace{11mu} {\max\limits_{a^{\prime}}\; {Q_{a^{\prime}}( b^{\prime} )}}} - {\alpha_{a} \cdot b}} )}$$\alpha_{a} = {\alpha_{a} + {\sum\limits_{j}{{{\Delta\alpha}_{a}( s_{j} )}b_{j}}}}$

(where μ is the update rate).

Another approximate technique is a Heuristic Search Value Iteration(HSVI) algorithm proposed in (Smith, and Simmons, 2004). This is ananytime algorithm that returns an approximate policy and a provablebound on its error with respect to optimal policy. HSVI combines twowell-known techniques: attention-focusing search heuristics andpiece-wise linear convex representations of the value function. On someof the benchmarking problems, HSVI displayed over 100 times improvementin solution time compared to state of the art POMDP value iterationalgorithms (Smith, and Simmons, 2004). In addition, HSVI was able tosolve problems 10 times larger that those reported previously. The HSVIalgorithm finds an approximate solution by recursively following asingle path down the search tree of the belief-action state space untilsatisfying a termination condition based on the error specification. Itthen performs a series of updates on its way back to initial beliefpoint.

Another algorithm for fast POMDP solution is an internal-statepolicy-gradient algorithm (Aberdeen, 2003). It was shown to solve theproblem with tens of thousands of possible environment states inreasonable time (30 minutes). This algorithm approximates the optimalPOMDP solution as the finite-state stochastic controller, in which theactions are selected via a stochastic parameterized policy μ(a|θ,g,z)equal to the probability of taking action a ε A given observation z ε Z,where g ε G is the internal state of the controller, and θ is the set ofparameters. The controller's internal states change from g to hprobabilistically after each observation z is received using statetransition probabilities ω(h|φ,g,z). The policy gradient algorithm findsthe coefficients (φ,θ) using the update rule:

φ_(k+1)=φ_(k)−α_(k)∇η(φ_(k),θ_(k)); θ_(k+1)=θ_(k)−β_(k)∇η(φ_(k),θ_(k))

where ∇η(φ,θ) is the gradient of the long-term average reward function

${\eta ( {\varphi,\theta} )} = {\lim\limits_{Tarrow\infty}{\frac{1}{T}{{E_{\varphi,\theta}\lbrack {\sum\limits_{t = 0}^{T}\; {r( i_{t} )}} \rbrack}.}}}$

These iterations are performed to maximize η(φ,θ) over parameters (φ,θ).The computation of the gradient estimate to replace ∇η(φ,θ) is the mainchallenge in finding the solution to POMDP.

The parameterization of functions ω(h|φ,g,z) and μ(a|θ,g,z) in terms ofparameters (φ,θ) can be different and will result in differentalgorithms. In (Λberdeen, 2003) the soft-max functions were used togenerate the distributions from real-valued output of a functionapproximator in the form of an artificial neural network. Morespecifically, the functions were defined as:

${{\omega ( {{h\varphi},g,z} )} = \frac{\exp ( \varphi_{goh} )}{\sum\limits_{h^{\prime} \in G}\; {\exp ( \varphi_{{goh}^{\prime}} )}}};$${\mu ( {{a\theta},g,z} )} = \frac{\exp ( \theta_{hoa} )}{\sum\limits_{a^{\prime} \in A}\; {\exp ( \theta_{{hoa}^{\prime}} )}}$

Here, the parameters φ_(goh),θ_(hoa) can be stored either in the look-uptables updated via the gradient method above, or using the artificialneural network (ANN). The gradient ∇η(φ,θ) can be expressed as∇η(φ,θ)=π′(∇P)[I−P+eπ′]⁻¹r.

Here, P(φ,θ) is a |S∥G|×|S∥G| transition matrix ofenvironment-controller pairs states with entries

${p( {j,{h},g,\varphi,\theta} )} = {\sum\limits_{z,a}\; {{o( {z} )}{\omega ( {{h\varphi},g,z} )}{\mu ( {{a\theta},g,z} )}{{\tau ( {{j},a} )}.}}}$

This matrix has a unique stationary distribution π(φ,θ) (a vector ofsize |S∥G| for all environment-controller state pairs, and

$  {{\sum\limits_{\underset{i \in G}{s \in S}}\; {\pi_{s,i}( {\varphi,\theta} )}} = 1} ) )$

such that π′(φ,θ)·P(φ,θ)=π′(φ,θ). Also e is a vector of all “1s” of size|S∥G|, hence eπ′ is a |S∥G|×|S∥G| matrix of π′(φ,θ) in each row. In theabove, r(i,g)=r(i), ∀g ε G—the function of reward of reaching trainingexpertise state i ε S.

The approximation of the gradient can be obtained using the following:

∇η≈∇_(n)η=π_(n)′(∇P)x _(n)

π_(n+1)′=π_(n) ′P

x _(n+1) =x _(n) +w _(n+1) ,x ₀ =r

w _(n+1) =Pw _(n)

Typical complexity, given the sparse matrices, isO(const|S∥G∥A|(n_(φ)+n_(θ))).

Note that this requires the knowledge of P, which in turn requiresknowing the “environment dynamics”. When it is not known, we can usereinforcement learning approaches to iteratively update the (φ,θ)together before each action is taken.

Use of Utility/Value Function:

The model can be made to stop iterating when a threshold is met such asstopping when no improvement in the objective function of expectedreward is obtained. A utility function can be used that describes thebenefit of gaining specific expertise by the team. A cost function canbe used to define the cost of training and scenario setup required for aspecific experiment. A value function can describe the overall expectedfuture reward of the training policy applied at a given state, where thereward is calculated based on probabilities of being in different statesof expertise in the future based on the training conducted using thepolicy, the benefits of those states, and the costs of conductingtraining experiments.

FIG. 7 shows how the value function approximation is conductediteratively using the assessments of possible future actions (trainingexperiments) and the states of expertise that a team could achieve. InFIG. 7 we show a 1-parameter (problem with 2 states of expertise, 2training scenario actions, and 2 observations; hence belief state spacecan be represented with a single variable—a probability of state 0 whichis between 0 and 1) expected value function estimate at iteration N onthe left hand side, which is a piece-wise linear function with supportareas for each linear component represented as an interval in the rangebetween 0 and 1. We can see how the space (between 0 and 1 in thisexample) of beliefs about the true team expertise state (=0 in thisexample) is separated into the regions where the same action (training)needs to be applied, and that this separation is updated over time. Notethat complexity of the belief state space split does often decreasesover time for some iterations.

The Resulting Decision making Policy:

The training policy obtained from the POMDP decision making system isdescribed as an interrelated policy graph, matrix or a look-up tablethat describes this interrelationship of variables and functions. Thepolicy is a finite state controller which consists of the policy nodes,where each policy node has an action (training scenario) associated withit. Policy nodes represent a subspace of beliefs about the true state ofthe team's expertise. The transition between policy nodes occurs basedon corresponding observations received after the training experimentusing the scenario is conducted.

FIG. 5 shows a simple example of a training policy 500 where the squaresindicate policy nodes such as 501, 502 and 503, and the nodes insidethem correspond to the actions, such as 544, that are taken in thosenodes. Transitions are indicated by the arrows with the observationswhich triggers them such as 592, 594 and 596. An example of how thepolicy works can be followed by the node 1. With node 502, action a2 544is performed. After this action is performed, an observation is made. Ifobservation z2 584 is made, the policy makes the decision that node 5505 should be the next node selected which has action a3 546. The actiona3 546 is performed resulting in an observation and the related node andaction is again followed. The process is repeated until a threshold ismet.

Define State of Subject to Start Application of Decision making Policy:

In a training domain, the true states that team expertise takes overtime (that is, states of MDP) are not known to the trainer or theinstructional model. They obtain only partial observations about currentstate of expertise in the form of performance and/or process measures.The observation-state relationships during training are captured usingthe training policy.

The training policy obtained by solving POMDP results in a tailoreddecision path for different teams, because it employs the observedperformance measurements on each team. Using the POMDP policy graph, atrainer picks a starting node (initial assessment of the teamknowledge), executes the instructional scenario associated with thecurrent node, receives the performance measures (observation) on theteam, selects an instructional scenario (transitions to the next node)based on the observation, and then repeats. The training process isrepeated until the node is reached without any outgoing transition linksor until a threshold is met.

Referring to FIG. 6, the steps of the process 600 that pertain to theapplication of the decision making process to subjects comprises thesteps of defining the belief state of a subject 650, selecting an actionfrom the decision making policy 660, applying that action 670, definingthe new belief state of the subject 680, determining whether a thresholdis met 690 and if the threshold is met, finishing the process 695. If itis determined that the threshold is not met in step 690, the steps of660, 670, 680 and 690 are repeated until the threshold is met. A moredetailed description of each of these steps is included below.

Selecting Actions Based on Training Policy:

In FIG. 5 the squares indicate policy nodes, and the nodes inside themcorrespond to the actions that are taken in those nodes. Transitions arethe links in this graph labeled with the observations which triggersthem.

Referring again to the process of FIG. 6, at step 650, the user (teamtrainer) identifies the belief state closely representing the currentstate of expertise of the team, and picks corresponding node in thepolicy.

Applying Actions:

Step 660 comprises identifying the action associated with the initialstate from step 650. Then with step 670, the first training isadministered to a team based on the action associated with this node.Applying the action from the policy is equivalent to conducting thetraining for the team corresponding to the scenario described in thisaction. Different scenarios can exist, varying by training duration,complexity of the experiment, the internal experiment objectives, typesof targets and their frequencies, experimental domain, etc.

Defining Changed State of the Subject by Observing Subjects:

After training on the scenario corresponding to selected policy action,the observations about team's performance are obtained as step 680.These observations consist of measures of process and performance of theteam. Based on what observations are obtained, the next policy node isselected.

Observations are equivalent to collecting the measures of theperformance and processes of the team during its execution of thetraining scenario. Such measures may include number and types of targetskilled, delays in information sharing and target prosecution,communication patterns among team members, the workload of team members,etc.

The changed states of team expertise correspond to the nodes in thepolicy graph. Policy nodes represent a subspace of beliefs about thetrue state of the team's expertise. Therefore, when the policy moved toone node from the other, this indicates that the team expertise could bein a certain range, but independent of specific expertise quantities thesame training should be applied to the team.

Comparing New Belief State to Threshold:

Although not always required, step 690 comprises comparing the state toa threshold to determine when to stop iterating through the process.

In some embodiments of the process 600, one of the ways to define thebelief subspace is to use the concept of thresholds. These are limitingvalues on the specific beliefs about the state of the expertise. Forexample, the belief subspace can be defined as “high expertise achievedwith probability between 50% and 80%”. The thresholds of 50% and 80%define the boundaries of the subspace.

It is useful to consider the shareholding at the initialization, whenthe starting node in the policy graph is selected to initializetraining. Comparison of the belief about team expertise to thethresholds in each policy node allows identifying what belief subspacethe current team expertise belongs to, and accordingly start thetraining from this policy node.

Stopping the Process:

The training is finished at step 695 when a node is reached with nooutgoing transition links, or when the training time deadline isreached.

The policy node with no outgoing transition links indicates that thestate of the expertise for a team has been reached that satisfiesoriginal training objectives. This is embedded in the POMDP solution andcalculation of the original policy graph.

The time deadline can be reached even if the training objectives mightnot be achieved. In this case, the training must be stopped. The teamexpertise that is declared will correspond to the belief subspace of thefinal policy node.

Testing Results Achieved:

Laboratory experiments were conducted to evaluate the POMDP solutionagainst a control condition: hierarchical part-task training.

Table 1 shows that the POMDP protocol, which adapted scenario selectionto the performance of the team, assigned scenarios with differentdifficulty levels (number of time-sensitive targets (TSTs) and Treats)than those predetermined in the Control protocol. The average TO3accuracy for the scenarios in the beginning (3&4), middle (5), and end(6&7) were 2.7, 3.8, and 3.1 for the POMDP protocol and 1.48, 3.00, and2.30 for the Control protocol. We did not test these differences becausethe difficulty levels were different and we had no predictions aboutthem.

TABLE 1 Difficulty Levels in Phase III Practice for POMDP and ControlProtocols Control POMDP Scenario TST Threat TST Threat 3 Early Practice11 33 10 35 4 Early Practice 12 33 11 40 5 Middle Practice 12 35 11 40 6Late Practice 12 40 12 45 7 Late Practice 12 45 12 45

The skill level during early training was higher than we had anticipatedcausing a ceiling effect during Phase II. Specifically, the meanaccuracy for TO3 increased from 2.9 to 3.5, as predicted, but theincrease was not significant (t(34)=1.38, p>0.05). In contrast, theratings of TO3 accuracy were sensitive measures between Phases II andIII, and within Phase III and they supported our predictions. Theceiling effect in Phase II was due to high performance on the pretest.

As FIG. 9 illustrates teams learned the complex task being trained(PreII T→PostII T, p<0.01); that administration of new and challengingproblems (i.e., far transfer) degraded performance (PostII T→Pre III P &C, p<0.01); (3) that, on far transfer problems, teams in hierarchicalpart-task condition (control) did not reliably learn (PreIII P→PostIIIP, p>0.05), while those in the BEST/POMDP condition did learn (PreIIIC→PostIII C, p<0.01). We used SPSS to compute means and conductconservative t-tests that do not assume equal variance. These statisticsfollow. Between the posttest in Phase II and the POMDP pretest in PhaseIII, the TO3 accuracy decreased significantly from 3.5 to 1.6(t(26)=4.38, p<0.01). On the POMDP posttest, TO3 accuracy rosesignificantly from 1.6 on the pretest to 3.0 on the posttest (t(31)=3.11, p<0.01). Between the POMDP posttest and the new challenge forthe Control Pretest, performance fell from 3.0 to 1.6 (t(27)=2.83,p<0.01). On the Control protocol Posttest, the slight rise from 1.6 to1.9 was not significant (t(34)=0.48, p>0.05). Note that the standarderrors for the posttests were consistently smaller than those for thepretests

FIG. 8 shows mean accuracy ratings for training objective 3 (TO3) fortheir sets of pretests and posttests for Phase II training (II T), PhaseIII POMDP Protocol (III P), and Phase III Control Protocol (III C). Thefigure illustrates (1: PreII T→PostII T) that teams learned the complextask being trained (p<0.01); (2: PostII T→PreIII P & C) thatadministration of novel problems (i.e., far transfer) degradesperformance (p<0.01); (3) that, on far transfer problems, (PreIIIP→PostIII P) teams in hierarchical part-task condition (control) do notreliably learn (p>0.05), while (PreIII C→Post III C) those in theBEST/POMDP condition do (p<0.01).

Description of a Computer Based Embodiment:

The described systems, methods, and techniques described may beimplemented in digital electronic circuitry, computer hardware,firmware, software, or in combinations of these elements. Apparatusembodying these techniques may include appropriate input and outputdevices, a computer processor, and a computer program product, ormachine instructions tangibly embodied in a machine-readable storagedevice for execution by a programmable processor. A process embodyingthese techniques may be performed by a programmable processor executinga program of machine instructions to perform desired functions byoperating on input data and generating appropriate output. Thetechniques may be implemented in one or more computer programs that areexecutable on a programmable system including at least one programmableprocessor coupled to receive data and instructions from, and to transmitdata and instructions to, a data storage system, at least one inputdevice, and at least one output device. Each computer program may beimplemented in a high-level procedural or object-oriented programminglanguage or in assembly or machine language if desired; and in any case,the language may be a compiled or interpreted language. Suitableprocessors include, by way of example, both general and special purposemicroprocessors. Generally, a processor will receive instructions anddata from a read-only memory and/or a random access memory. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as Erasable ProgrammableRead-Only Memory (EPROM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCompact Disc Read-Only Memory (CD-ROM). Any of the foregoing may besupplemented by, or incorporated in, specially-designed ASICs(application-specific integrated circuits).

A functional diagram of one embodiment of the machine instructions thatcreate a decision making system is shown in FIG. 9 FIG. 9 comprises thesoftware modules: setup 910, POMDP model 920, assessment 930, trainingcontroller 940, data collection 950 and training system 960.

The setup module 910 is used to define variables used in the processsuch as, but not limited to the set of training actions 911, objectives912 (used to define the rewards in POMDP model), domain specification913, and possibly a type of the team to be trained 914. It is understoodthat different POMDPs can be defined for different types of teams.

The POMDP model module 920 is used to define the POMDP model and developthe solution policy. POMDP model consists of states of expertise 924,which in one embodiment can be defined by the user, the set of actions925 (defined from the training scenarios), feasible observations 926(based on the measures of process and performance using during thetraining process), and functions 928 a and 928 b, including the priorprobability 927, state transition probability, observation probability,and cost functions 929. POMDP model module includes a library of POMDPsolution algorithms 923, all of which generate the POMDP solution policybut may find solution in different ways (e.g., trading off thecomplexity and use of memory with optimality). The POMDP solutionsinternally rely on definitions of belief state space 921 (example of thestatespace update is illustrated in FIG. 4).

The assessment module 930 generates the observation and rewardestimations using the measures engine 932, the observation estimator 933and the reward estimator 934. This engine takes as inputs the trainingvignette event flow and computes a set of measures (possiblytime-dependent) using the measures library 931. These measures areselected from the training objectives and defined manually by the user.The assessment module feeds the observations and rewards into trainingcontroller module 940.

The training controller module 940 stores and updates the trainingpolicy 944. It is used for the selection of the next training scenariofor the team through the scenario loader 942. It can update the trainingpolicy using the policy update function 941. Training policy allows thiscomponent to obtain the specs 943 of the training scenario for the nexttraining session for the team, and use this specification to retrievethe actual training scenario vignette to give to the trainees.

The data collection module 950 is setup to extract and import the eventsfrom the training simulation and store these events in the event store952.

The training system module 960 can be a virtual environment presentedfrom a server 961 or any other method of training the subjects. It takesas inputs the training scenario from the training controller 940,provides the training to the team through a scenario subsystem 963, andgenerates the event stream corresponding to the training experiences.The training system can comprise a system with multiple clients, 963,964 and 965.

This invention is not limited to the methods and systems described inthe embodiments above. The methods of this invention are easilyincorporated into computer systems and data networks that allow certainsteps of these methods, such as input and output, to be performed onclient machines connected to a computer network while the computationalsteps and data set storage can be done through a server in a clientserver model or other distributed computing architecture. It is alsoenvisioned that the methods can be used over a wireless computer networkto include wireless computers, wireless phones or other wireless datanetwork.

Therefore, the foregoing is considered as illustrative only of theprinciples of the invention. Further, since numerous modifications andchanges will readily occur to those skilled in the art, it is notdesired to limit the invention to the exact construction and operationshown and described, and accordingly, all suitable modifications andequivalents may be resorted to, falling within the scope of theinvention. Although this invention has been described in the above formswith a certain degree of particularity, it is understood that thepresent disclosure has been made only by way of example and numerouschanges in the details of construction and combination and arrangementof parts may be resorted to without departing from the spirit and scopeof the invention.

1. A computer based system for determining training treatments for asubject, said system comprising: a memory to store at least one actioncomprising at least one training treatment; a processor capable ofexecuting machine instructions; and the machine instructions includingmeans for executing a POMDP model to create a training policy todetermine the at least one training treatment to train a subject on atopic.
 2. The system of claim 1 wherein the subject is a team and thetraining treatments are training treatments for team training.
 3. Thesystem of claim 1 wherein the means for executing a POMDP model furthercomprises the POMDP model having at least one state, at least onetransition function, at least one reward function, at least oneobservation and at least one observation function.
 4. The system ofclaim 3 wherein: the at least one state comprises a representation of anexpertise state of the subject; the at least one transition functioncomprises a representation of the probability of an expected changedexpertise state of the subject after training the subject on thetreatment; the at least one reward function comprises a representationof at least one objective and at least one cost of training the subjecton the treatment; the at least one observation comprises arepresentation of a measure of the subject; and the at least oneobservation function comprises a representation of the probability of anexpected observation of the subject after training the subject on thetreatment.
 5. The system of claim 4 wherein the representation of thestate of expertise of the subject comprises a set of numbersrepresenting the expertise state of the subject on the topic.
 6. Thesystem of claim 4 wherein the observation comprises a set of numbersrepresenting the measures of the subject.
 7. The system of claim 4wherein: the transition function representation of the expected changedexpertise state of the subject further comprises a probability of movingfrom the expertise state to the expected changed expertise stateconditioned on the training treatment given to a subject; the rewardfunction representation of the objective further comprises at least onenumber, where each number represents a benefit of subject attaining theexpertise state given the training treatment; and the observationfunction representation of the expected observation of the subjectfurther comprises the probability of an observation given the subject'sexpertise state and training treatment given to the subject.
 8. Thesystem of claim 4 wherein the step of utilizing a POMDP model furthercomprises creating a training policy by linking each state to at leastone training treatment at a node and interconnecting each node toanother node by at least one observation.
 9. The system of claim 8wherein the step of utilizing a POMDP model further comprises applyingthe training policy by obtaining the state of the subject, selecting thenode having that state and determining the linked training treatment atthat node as the training treatment to train the subject on the topic.10. The system of claim 9 further comprising: after the step of applyingthe training policy to determine the training treatment, training thesubject on the training treatment; obtaining the observation for thesubject; and applying the training policy to select the interconnectednode and the changed state of the subject based on the observation, anddetermine a next training treatment to train the subject.
 11. The systemof claim 10 further comprising: a process threshold; and iterating thesteps of training the subject, obtaining the observation for the subjectand applying the training policy to select the training treatment untilthe process threshold is met.
 12. A program storage device readable by amachine, tangibly embodying a program of instructions executable by themachine to perform the method steps comprising: generating a decisionmaking policy from a POMDP model; the POMDP model comprising at leastone state parameter, at least one observation parameter and at least oneaction parameter; and the action parameter comprising trainingtreatments.
 13. The program storage device of claim 12 wherein the atleast one state parameter comprises the state of expertise of a subjectand the at least one observation parameter comprises a measure of theexpertise of the subject.
 14. The program storage device of claim 13wherein the step of generating a decision making policy furthercomprises: defining the at least one state parameter, the at least oneaction parameter and the at least one observation parameter; defining aplurality of functions comprising at least one transition function, atleast one observation function and at least one utility function; andgenerating the decision making policy based on said parameters and saidfunctions.
 15. The program storage device of claim 14 furthercomprising; determining a changed state of the subject after applying anaction parameter; comparing the changed state of the subject to aprocess threshold; selecting the at least one action parameter from thedecision making policy; applying the at least one action parameter tothe subject; determining a new changed state of the subject; comparingthe new changed state of the subject to the process threshold; andrepeating the steps of selecting the at least one action parameter,applying the at least one action parameter, determining a new changedstate and comparing the new changed state until the process threshold ismet.
 16. A computer based method for structuring training treatments fora subject on a topic, said method comprising: defining at least oneaction comprising at least one training treatment; utilizing a POMDPmodel to create a training policy to determine the at least one trainingtreatment to train a subject on a topic.
 17. The method of claim 16wherein the subject is a team and the training treatments are trainingtreatments for team training.
 18. The method of claim 16 wherein thestep of utilizing a POMDP model further comprises the POMDP model havingat least one state, at least one transition function, at least onereward function, at least one observation and at least one observationfunction.
 19. The method of claim 18 wherein: the at least one statecomprises a representation of an expertise state of the subject; the atleast one transition function comprises a representation of theprobability of an expected changed expertise state of the subject aftertraining the subject on the treatment; the at least one reward functioncomprises a representation of at least one objective and at least onecost of training the subject on the treatment; the at least oneobservation comprises a representation of a measure of the subject; andthe at least one observation function comprises a representation of theprobability of an expected observation of the subject after training thesubject on the treatment.
 20. The method of claim 19 wherein therepresentation of the state of expertise of the subject comprises a setof numbers representing the expertise state of the subject on the topic.21. The method of claim 19 wherein the observation comprises a set ofnumbers representing the measures of the subject.
 22. The method ofclaim 19 wherein: the transition function representation of the expectedchanged expertise state of the subject further comprises a probabilityof moving from the expertise state to the expected changed expertisestate conditioned on the training treatment given to a subject; thereward function representation of the objective further comprises atleast one number, where each number represents a benefit of subjectattaining the expertise state given the training treatment; and theobservation function representation of the expected observation of thesubject further comprises the probability of an observation given thesubject's expertise state and training treatment given to the subject;23. The method of claim 19 wherein the step of utilizing a POMDP modelfurther comprises creating a training policy by linking each state to atleast one training treatment at a node and interconnecting each node toanother node by at least one observation.
 24. The method of claim 23wherein the step of utilizing a POMDP model further comprises applyingthe training policy by obtaining the state of the subject, selecting thenode having that state and determining the linked training treatment atthat node as the training treatment to train the subject on the topic.25. The method of claim 24 further comprising: after the step ofapplying the training policy to determine the training treatment,training the subject on the training treatment; obtaining theobservation for the subject; and applying the training policy to selectthe interconnected node and the changed state of the subject based onthe observation, and determine a next training treatment to train thesubject.
 26. The method of claim 25 further comprising: a processthreshold; and iterating the steps of training the subject, obtainingthe observation for the subject and applying the training policy toselect the training treatment until the process threshold is met.